Navigation

The pickle module implements a fundamental, but powerful algorithm for
serializing and de-serializing a Python object structure. “Pickling” is the
process whereby a Python object hierarchy is converted into a byte stream, and
“unpickling” is the inverse operation, whereby a byte stream is converted back
into an object hierarchy. Pickling (and unpickling) is alternatively known as
“serialization”, “marshalling,” [1] or “flattening”, however, to avoid
confusion, the terms used here are “pickling” and “unpickling”.

This documentation describes both the pickle module and the
cPickle module.

Warning

The pickle module is not intended to be secure against erroneous or
maliciously constructed data. Never unpickle data received from an untrusted
or unauthenticated source.

The pickle module has an optimized cousin called the cPickle
module. As its name implies, cPickle is written in C, so it can be up to
1000 times faster than pickle. However it does not support subclassing
of the Pickler() and Unpickler() classes, because in cPickle
these are functions, not classes. Most applications have no need for this
functionality, and can benefit from the improved performance of cPickle.
Other than that, the interfaces of the two modules are nearly identical; the
common interface is described in this manual and differences are pointed out
where necessary. In the following discussions, we use the term “pickle” to
collectively describe the pickle and cPickle modules.

The data streams the two modules produce are guaranteed to be interchangeable.

Python has a more primitive serialization module called marshal, but in
general pickle should always be the preferred way to serialize Python
objects. marshal exists primarily to support Python’s .pyc
files.

The pickle module keeps track of the objects it has already serialized,
so that later references to the same object won’t be serialized again.
marshal doesn’t do this.

This has implications both for recursive objects and object sharing. Recursive
objects are objects that contain references to themselves. These are not
handled by marshal, and in fact, attempting to marshal recursive objects will
crash your Python interpreter. Object sharing happens when there are multiple
references to the same object in different places in the object hierarchy being
serialized. pickle stores such objects only once, and ensures that all
other references point to the master copy. Shared objects remain shared, which
can be very important for mutable objects.

marshal cannot be used to serialize user-defined classes and their
instances. pickle can save and restore class instances transparently,
however the class definition must be importable and live in the same module as
when the object was stored.

The marshal serialization format is not guaranteed to be portable
across Python versions. Because its primary job in life is to support
.pyc files, the Python implementers reserve the right to change the
serialization format in non-backwards compatible ways should the need arise.
The pickle serialization format is guaranteed to be backwards compatible
across Python releases.

Note that serialization is a more primitive notion than persistence; although
pickle reads and writes file objects, it does not handle the issue of
naming persistent objects, nor the (even more complicated) issue of concurrent
access to persistent objects. The pickle module can transform a complex
object into a byte stream and it can transform the byte stream into an object
with the same internal structure. Perhaps the most obvious thing to do with
these byte streams is to write them onto a file, but it is also conceivable to
send them across a network or store them in a database. The module
shelve provides a simple interface to pickle and unpickle objects on
DBM-style database files.

The data format used by pickle is Python-specific. This has the
advantage that there are no restrictions imposed by external standards such as
XDR (which can’t represent pointer sharing); however it means that non-Python
programs may not be able to reconstruct pickled Python objects.

By default, the pickle data format uses a printable ASCII representation.
This is slightly more voluminous than a binary representation. The big
advantage of using printable ASCII (and of some other characteristics of
pickle‘s representation) is that for debugging or recovery purposes it is
possible for a human to read the pickled file with a standard text editor.

There are currently 3 different protocols which can be used for pickling.

Protocol version 0 is the original ASCII protocol and is backwards compatible
with earlier versions of Python.

Protocol version 1 is the old binary format which is also compatible with
earlier versions of Python.

Protocol version 2 was introduced in Python 2.3. It provides much more
efficient pickling of new-style classes.

To serialize an object hierarchy, you first create a pickler, then you call the
pickler’s dump() method. To de-serialize a data stream, you first create
an unpickler, then you call the unpickler’s load() method. The
pickle module provides the following constant:

The highest protocol version available. This value can be passed as a
protocol value.

New in version 2.3.

Note

Be sure to always open pickle files created with protocols >= 1 in binary mode.
For the old ASCII-based pickle protocol 0 you can use either text mode or binary
mode as long as you stay consistent.

A pickle file written with protocol 0 in binary mode will contain lone linefeeds
as line terminators and therefore will look “funny” when viewed in Notepad or
other editors which do not support this format.

The pickle module provides the following functions to make the pickling
process more convenient:

Read a string from the open file object file and interpret it as a pickle data
stream, reconstructing and returning the original object hierarchy. This is
equivalent to Unpickler(file).load().

file must have two methods, a read() method that takes an integer
argument, and a readline() method that requires no arguments. Both
methods should return a string. Thus file can be a file object opened for
reading, a StringIO object, or any other custom object that meets this
interface.

This function automatically determines whether the data stream was written in
binary mode or not.

This exception is raised when there is a problem unpickling an object. Note that
other exceptions may also be raised during unpickling, including (but not
necessarily limited to) AttributeError, EOFError,
ImportError, and IndexError.

Write a pickled representation of obj to the open file object given in the
constructor. Either the binary or ASCII format will be used, depending on the
value of the protocol argument passed to the constructor.

Clears the pickler’s “memo”. The memo is the data structure that remembers
which objects the pickler has already seen, so that shared or recursive objects
pickled by reference and not by value. This method is useful when re-using
picklers.

Note

Prior to Python 2.3, clear_memo() was only available on the picklers
created by cPickle. In the pickle module, picklers have an
instance variable called memo which is a Python dictionary. So to clear
the memo for a pickle module pickler, you could do the following:

mypickler.memo.clear()

Code that does not need to support older versions of Python should simply use
clear_memo().

It is possible to make multiple calls to the dump() method of the same
Pickler instance. These must then be matched to the same number of
calls to the load() method of the corresponding Unpickler
instance. If the same object is pickled by multiple dump() calls, the
load() will all yield references to the same object. [3]

This takes a file-like object from which it will read a pickle data stream.
This class automatically determines whether the data stream was written in
binary mode or not, so it does not need a flag as in the Pickler
factory.

file must have two methods, a read() method that takes an integer
argument, and a readline() method that requires no arguments. Both
methods should return a string. Thus file can be a file object opened for
reading, a StringIO object, or any other custom object that meets this
interface.

This is just like load() except that it doesn’t actually create any
objects. This is useful primarily for finding what’s called “persistent
ids” that may be referenced in a pickle data stream. See section
The pickle protocol below for more details.

Attempts to pickle unpicklable objects will raise the PicklingError
exception; when this happens, an unspecified number of bytes may have already
been written to the underlying file. Trying to pickle a highly recursive data
structure may exceed the maximum recursion depth, a RuntimeError will be
raised in this case. You can carefully raise this limit with
sys.setrecursionlimit().

Note that functions (built-in and user-defined) are pickled by “fully qualified”
name reference, not by value. This means that only the function name is
pickled, along with the name of the module the function is defined in. Neither
the function’s code, nor any of its function attributes are pickled. Thus the
defining module must be importable in the unpickling environment, and the module
must contain the named object, otherwise an exception will be raised. [4]

Similarly, classes are pickled by named reference, so the same restrictions in
the unpickling environment apply. Note that none of the class’s code or data is
pickled, so in the following example the class attribute attr is not
restored in the unpickling environment:

classFoo:attr='a class attr'picklestring=pickle.dumps(Foo)

These restrictions are why picklable functions and classes must be defined in
the top level of a module.

Similarly, when class instances are pickled, their class’s code and data are not
pickled along with them. Only the instance data are pickled. This is done on
purpose, so you can fix bugs in a class or add methods to the class and still
load objects that were created with an earlier version of the class. If you
plan to have long-lived objects that will see many versions of a class, it may
be worthwhile to put a version number in the objects so that suitable
conversions can be made by the class’s __setstate__() method.

This section describes the “pickling protocol” that defines the interface
between the pickler/unpickler and the objects that are being serialized. This
protocol provides a standard way for you to define, customize, and control how
your objects are serialized and de-serialized. The description in this section
doesn’t cover specific customizations that you can employ to make the unpickling
environment slightly safer from untrusted pickle data streams; see section
Subclassing Unpicklers for more details.

When a pickled class instance is unpickled, its __init__() method is
normally not invoked. If it is desirable that the __init__() method
be called on unpickling, an old-style class can define a method
__getinitargs__(), which should return a tuple containing the
arguments to be passed to the class constructor (__init__() for
example). The __getinitargs__() method is called at pickle time; the
tuple it returns is incorporated in the pickle for the instance.

New-style types can provide a __getnewargs__() method that is used for
protocol 2. Implementing this method is needed if the type establishes some
internal invariants when the instance is created, or if the memory allocation
is affected by the values passed to the __new__() method for the type
(as it is for tuples and strings). Instances of a new-style classC are created using

Classes can further influence how their instances are pickled; if the class
defines the method __getstate__(), it is called and the return state is
pickled as the contents for the instance, instead of the contents of the
instance’s dictionary. If there is no __getstate__() method, the
instance’s __dict__ is pickled.

Upon unpickling, if the class also defines the method __setstate__(),
it is called with the unpickled state. [5] If there is no
__setstate__() method, the pickled state must be a dictionary and its
items are assigned to the new instance’s dictionary. If a class defines both
__getstate__() and __setstate__(), the state object needn’t be a
dictionary and these methods can do what they want. [6]

When the Pickler encounters an object of a type it knows nothing
about — such as an extension type — it looks in two places for a hint of
how to pickle it. One alternative is for the object to implement a
__reduce__() method. If provided, at pickling time __reduce__()
will be called with no arguments, and it must return either a string or a
tuple.

If a string is returned, it names a global variable whose contents are
pickled as normal. The string returned by __reduce__() should be the
object’s local name relative to its module; the pickle module searches the
module namespace to determine the object’s module.

When a tuple is returned, it must be between two and five elements long.
Optional elements can either be omitted, or None can be provided as their
value. The contents of this tuple are pickled as normal and used to
reconstruct the object at unpickling time. The semantics of each element
are:

A callable object that will be called to create the initial version of the
object. The next element of the tuple will provide arguments for this
callable, and later elements provide additional state information that will
subsequently be used to fully reconstruct the pickled data.

In the unpickling environment this object must be either a class, a
callable registered as a “safe constructor” (see below), or it must have an
attribute __safe_for_unpickling__ with a true value. Otherwise, an
UnpicklingError will be raised in the unpickling environment. Note
that as usual, the callable itself is pickled by name.

Optionally, an iterator (and not a sequence) yielding successive list
items. These list items will be pickled, and appended to the object using
either obj.append(item) or obj.extend(list_of_items). This is
primarily used for list subclasses, but may be used by other classes as
long as they have append() and extend() methods with the
appropriate signature. (Whether append() or extend() is used
depends on which pickle protocol version is used as well as the number of
items to append, so both must be supported.)

Optionally, an iterator (not a sequence) yielding successive dictionary
items, which should be tuples of the form (key,value). These items
will be pickled and stored to the object using obj[key]=value. This
is primarily used for dictionary subclasses, but may be used by other
classes as long as they implement __setitem__().

An alternative to implementing a __reduce__() method on the object to be
pickled, is to register the callable with the copy_reg module. This
module provides a way for programs to register “reduction functions” and
constructors for user-defined types. Reduction functions have the same
semantics and interface as the __reduce__() method described above, except
that they are called with a single argument, the object to be pickled.

The registered constructor is deemed a “safe constructor” for purposes of
unpickling as described above.

For the benefit of object persistence, the pickle module supports the
notion of a reference to an object outside the pickled data stream. Such
objects are referenced by a “persistent id”, which is just an arbitrary string
of printable ASCII characters. The resolution of such names is not defined by
the pickle module; it will delegate this resolution to user defined
functions on the pickler and unpickler. [7]

To define external persistent id resolution, you need to set the
persistent_id attribute of the pickler object and the
persistent_load attribute of the unpickler object.

To pickle objects that have an external persistent id, the pickler must have a
custom persistent_id() method that takes an object as an
argument and returns either None or the persistent id for that object.
When None is returned, the pickler simply pickles the object as normal.
When a persistent id string is returned, the pickler will pickle that string,
along with a marker so that the unpickler will recognize the string as a
persistent id.

To unpickle external objects, the unpickler must have a custom
persistent_load() function that takes a persistent id string
and returns the referenced object.

In the cPickle module, the unpickler’s persistent_load
attribute can also be set to a Python list, in which case, when the unpickler
reaches a persistent id, the persistent id string will simply be appended to
this list. This functionality exists so that a pickle data stream can be
“sniffed” for object references without actually instantiating all the objects
in a pickle.
[8] Setting persistent_load to a list is usually used in
conjunction with the noload() method on the Unpickler.

By default, unpickling will import any class that it finds in the pickle data.
You can control exactly what gets unpickled and what gets called by customizing
your unpickler. Unfortunately, exactly how you do this is different depending
on whether you’re using pickle or cPickle. [9]

In the pickle module, you need to derive a subclass from
Unpickler, overriding the load_global() method.
load_global() should read two lines from the pickle data stream where the
first line will the name of the module containing the class and the second line
will be the name of the instance’s class. It then looks up the class, possibly
importing the module and digging out the attribute, then it appends what it
finds to the unpickler’s stack. Later on, this class will be assigned to the
__class__ attribute of an empty class, as a way of magically creating an
instance without calling its class’s __init__(). Your job (should you
choose to accept it), would be to have load_global() push onto the
unpickler’s stack, a known safe version of any class you deem safe to unpickle.
It is up to you to produce such a class. Or you could raise an error if you
want to disallow all unpickling of instances. If this sounds like a hack,
you’re right. Refer to the source code to make this work.

Things are a little cleaner with cPickle, but not by much. To control
what gets unpickled, you can set the unpickler’s find_global
attribute to a function or None. If it is None then any attempts to
unpickle instances will raise an UnpicklingError. If it is a function,
then it should accept a module name and a class name, and return the
corresponding class object. It is responsible for looking up the class and
performing any necessary imports, and it may raise an error to prevent
instances of the class from being unpickled.

The moral of the story is that you should be really careful about the source of
the strings your application unpickles.

Here’s a larger example that shows how to modify pickling behavior for a class.
The TextReader class opens a text file, and returns the line number and
line contents each time its readline() method is called. If a
TextReader instance is pickled, all attributes except the file object
member are saved. When the instance is unpickled, the file is reopened, and
reading resumes from the last location. The __setstate__() and
__getstate__() methods are used to implement this behavior.

#!/usr/local/bin/pythonclassTextReader:"""Print and number lines in a text file."""def__init__(self,file):self.file=fileself.fh=open(file)self.lineno=0defreadline(self):self.lineno=self.lineno+1line=self.fh.readline()ifnotline:returnNoneifline.endswith("\n"):line=line[:-1]return"%d: %s"%(self.lineno,line)def__getstate__(self):odict=self.__dict__.copy()# copy the dict since we change itdelodict['fh']# remove filehandle entryreturnodictdef__setstate__(self,dict):fh=open(dict['file'])# reopen filecount=dict['lineno']# read from file...whilecount:# until line count is restoredfh.readline()count=count-1self.__dict__.update(dict)# update attributesself.fh=fh# save the file object

The cPickle module supports serialization and de-serialization of Python
objects, providing an interface and functionality nearly identical to the
pickle module. There are several differences, the most important being
performance and subclassability.

First, cPickle can be up to 1000 times faster than pickle because
the former is implemented in C. Second, in the cPickle module the
callables Pickler() and Unpickler() are functions, not classes.
This means that you cannot use them to derive custom pickling and unpickling
subclasses. Most applications have no need for this functionality and should
benefit from the greatly improved performance of the cPickle module.

The pickle data stream produced by pickle and cPickle are
identical, so it is possible to use pickle and cPickle
interchangeably with existing pickles. [10]

There are additional minor differences in API between cPickle and
pickle, however for most applications, they are interchangeable. More
documentation is provided in the pickle module documentation, which
includes a list of the documented differences.

In the pickle module these callables are classes, which you could
subclass to customize the behavior. However, in the cPickle module these
callables are factory functions and so cannot be subclassed. One common reason
to subclass is to control what objects can actually be unpickled. See section
Subclassing Unpicklers for more details.

Warning: this is intended for pickling multiple objects without intervening
modifications to the objects or their parts. If you modify an object and then
pickle it again using the same Pickler instance, the object is not
pickled again — a reference to it is pickled and the Unpickler will
return the old value, not the modified one. There are two problems here: (1)
detecting changes, and (2) marshalling a minimal set of changes. Garbage
Collection may also become a problem here.

The actual mechanism for associating these user defined functions is slightly
different for pickle and cPickle. The description given here
works the same for both implementations. Users of the pickle module
could also use subclassing to effect the same results, overriding the
persistent_id() and persistent_load() methods in the derived
classes.

A word of caution: the mechanisms described here use internal attributes and
methods, which are subject to change in future versions of Python. We intend to
someday provide a common interface for controlling this behavior, which will
work in either pickle or cPickle.

Since the pickle data format is actually a tiny stack-oriented programming
language, and some freedom is taken in the encodings of certain objects, it is
possible that the two modules produce different data streams for the same input
objects. However it is guaranteed that they will always be able to read each
other’s data streams.